The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

EVAPOTRANSPIRATION MEASUREMENTS AND MODELING FOR THREE WETLAND SYSTEMS IN SOUTH FLORIDA1

ABSTRACT: At the Everglades Nutrient Removal project in south Florida, three lysimeters were installed to measure daily evapotranspiration (ET) rates from cattails (Typha domingensis), mixed marsh vegetation, and an open water/algae system. The cattail lysimeter began operation in February 1993. The mixed marsh vegetation lysimeter began operation in January 1994, and the open water lysimeter with occasional algae cover began operation in December 1993. The mean measured ET rate was 3.6 mm, 3.5 mm, and 3.7 mm per day for the cattail, mixed marsh vegetation, and open water/algae system, respectively. High resolution weather data were continuously measured at the site. Six models were applied to estimate daily ET rates of the three systems. The Penman-Monteith equation best estimated ET of cattail and mixed marsh vegetation, and the Penman Combination equation was most suitable for the open water/algae system. Empirical equations based on solar radiation and maximum temperature produced estimates of daily ET from the three systems that are comparable to models that require many more parameters. In cases where limited data is available, the calibrated simple models can be used to estimate ET from wetlands in south Florida.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

EVAPOTRANSPIRATION AND CANOPY RESISTANCE AT AN UNDEVELOPED PRAIRIE IN A HUMID SUBTROPICAL CLIMATE1

ABSTRACT: Reliable estimates of evapotranspiration from areas of wildland vegetation are needed for many types of water-resource investigations. However, little is known about surface fluxes from many areally important vegetation types, and relatively few comparisons have been made to examine how well evapotranspiration models can predict evapotranspiration for soil-, climate-, or vegetation-types that differ from those under which the models have been calibrated. In this investigation at a prairie site in west-central Florida, latent heat flux (λE) computed from the energy balance and alternatively by eddy covariance during a 15-month period differed by 4 percent and 7 percent on hourly and daily time scales, respectively. Annual evapotranspiration computed from the energy balance and by eddy covariance were 978 and 944 mm, respectively. An hourly Penman-Monteith (PM) evapotranspiration model with stomatal control predicated on water-vapor-pressure deficit at canopy level, incoming solar radiation intensity, and soil water deficit was developed and calibrated using surface fluxes from eddy covariance. Model-predicted λE agreed closely with λE computed from the energy balance except when moisture from dew or precipitation covered vegetation surfaces. Finally, an hourly PM model developed for an Amazonian pasture predicted λE for the Florida prairie with unexpected reliability. Additional comparisons of PM-type models that have been developed for differing types of short vegetation could aid in assessing interchangeability of such models.     

Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States1

Tobin, Kenneth J. and Marvin E. Bennett, 2012. Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States. Journal of the American Water Resources Association (JAWRA) 48(2): 221‐234. DOI: 10.1111/j.1752‐1688.2011.00604.x Abstract: The precipitation science community has expressed concern regarding the ability of satellite‐based precipitation products to accurately capture rainfall values over land. There has been some work that has focused on addressing the deficiencies of satellite precipitation products, particularly on the adjustment of bias. This article outlines a methodology that adjusts satellite products utilizing ground‐based precipitation data. The approach is not a simple bias adjustment, but is a three‐step process that transforms a satellite product based on a ground‐based precipitation product (NEXRAD‐derived Multisensor Precipitation Estimator [MPE] product or rain‐gauge data). The developed methodology was successfully applied to seven moderate‐to‐large sized watersheds from continental United States (CONUS) and northern Mexico over a spectrum of climatic regimes ranging from dry to humid settings. Methodology validation is based on comparison of observed and simulated streamflow generated with SWAT (Soil and Water Assessment Tool) model using unadjusted and adjusted precipitation products as input. Streamflow comparison is based on mass balance error and Nash‐Sutcliffe efficiency coefficient. Finally, the contribution of how adjustment to correct misses, false alarms, and bias impacts adjusted datasets and the potential impact that the adjustment methodology can have on hydrological applications such as water resource monitoring and flood prediction are explored.     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.     

Performance of a small-scale solar-powered adsorption cooling system

In this study, an experimental investigation on the performance of a small-scale residential-size solar-driven adsorption (silica gel-water) cooling system that was constructed at Assiut University campus, Egypt is carried out. As Assiut area is considered as hot, arid climate, field tests for performance assessment of the system operation during the summer season are performed under different environmental operating conditions. The system consists of an evacuated tube with a reflective concentration parabolic surface solar-collector field with a total area of 36 m², a silica gel-water adsorption chiller of 8 kW nominal cooling capacity, and hot and cold water thermal storage tanks of 1.8 and 1.2 m³ in volume, respectively. The results of summer season field test show that under daily solar insolation varying from 21 to 27 MJ/m², the solar collectors employed in the system had high and almost constant thermal efficiency. The daily solar-collector efficiency during the period of system operation ranged from about 50% to 78%. The adsorption chiller performance shows that the chiller average daily coefficient of performance (COP) was 0.41 with the average cooling capacity of 4.4 kW when the cooling-water and chilled-water temperatures were about 31°C and 19°C, respectively. As the chiller cooling water is cooled by the cooling tower in the hot arid area, the cooling water is at a higher temperature than the design point of the chiller. Therefore, an experiment was carried out using the city water for cooling. The results show that an enhancement in the chiller COP by 40% and the chilling power by 17% has been achieved when the city water was 27.7°C.     

Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation1

Greenberg, Jonathan Asher, Erin L. Hestir, David Riano, George J. Scheer, and Susan L. Ustin, 2012. Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation. Journal of the American Water Resources Association (JAWRA) 48(5): 939‐948. DOI: 10.1111/j.1752‐1688.2012.00664.x Abstract: Riparian vegetation provides shade from insolation to stream channels. A consequence of removing vegetation may be an increase in insolation that can increase water temperatures and negatively impact ecosystem health. Although the mechanisms of riparian shading are well understood, spatially explicit, mechanistic models of shading have been limited by the data requirements of precisely describing the three‐dimensional structure of a riparian corridor. Remotely acquired, high spatial resolution LiDAR data provide detailed three‐dimensional vegetation structure and terrain topography over large regions. By parameterizing solar radiation models that incorporate terrain shadowing with LiDAR data, we can produce spatially explicit estimates of insolation. As a case study, we modeled the relative change in insolation on channels in the Sacramento‐San Joaquin River Delta under current conditions and under a hypothesized deforested Delta using classified LiDAR, rasterized at a 1‐m resolution. Our results suggest that the removal of levee vegetation could result in a 9% increase in solar radiation incident on Delta waters, and may lead to water temperature increases. General, coarse‐scale channel characteristics (reach width, azimuth, levee vegetation cover, and height) only accounted for 72% of the variation in the insolation. This indicates that the detailed information derived from LiDAR data has greater explanatory power than coarser reach‐scale metrics often used for insolation estimates.     

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010 Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.     

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

This study applied three statistical downscaling methods: (1) bias correction and spatial disaggregation at daily time scale (BCSD_daily); (2) a modified version of BCSD which reverses the order of spatial disaggregation and bias correction (SDBC), and (3) the bias correction and stochastic analog method (BCSA) to downscale general circulation model daily precipitation outputs to the subbasin scale for west‐central Florida. Each downscaled climate input dataset was then used in an integrated hydrologic model to examine differences in ability to simulate retrospective streamflow characteristics. Results showed the BCSD_daily method consistently underestimated mean streamflow because the highly spatially correlated small precipitation events produced by this method resulted in overestimation of evapotranspiration. Highly spatially correlated large precipitation events produced by the SDBC method resulted in overestimation of the standard deviation of wet season daily streamflow and the magnitude/frequency of high streamflow events. BCSA showed better performance than the other methods in reproducing spatiotemporal statistics of daily precipitation and streamflow. This study demonstrated differences in statistical downscaling techniques propagate into significant differences in streamflow predictions, and underscores the need to carefully select a downscaling method that reproduces precipitation characteristics important for the hydrologic system under consideration.     

Modeling Hydrology in a Small Rocky Mountain Watershed Serving Large Urban Populations1

Abstract: This article describes the development of a calibrated hydrologic model for the Blue River watershed (867 km²) in Summit County, Colorado. This watershed provides drinking water to over a third of Colorado’s population. However, more research on model calibration and development for small mountain watersheds is needed. This work required integration of subsurface and surface hydrology using GIS data, and included aspects unique to mountain watersheds such as snow hydrology, high ground‐water gradients, and large differences in climate between the headwaters and outlet. Given the importance of this particular watershed as a major urban drinking‐water source, the rapid development occurring in small mountain watersheds, and the importance of Rocky Mountain water in the arid and semiarid West, it is useful to describe calibrated watershed modeling efforts in this watershed. The model used was Soil and Water Assessment Tool (SWAT). An accurate model of the hydrologic cycle required incorporation of mountain hydrology‐specific processes. Snowmelt and snow formation parameters, as well as several ground‐water parameters, were the most important calibration factors. Comparison of simulated and observed streamflow hydrographs at two U.S. Geological Survey gaging stations resulted in good fits to average monthly values (0.71 Nash‐Sutcliffe coefficient). With this capability, future assessments of point‐source and nonpoint‐source pollutant transport are possible.     

Global solar radiation and energy yield estimation from photovoltaic power plants for small loads

Daily global solar radiation on a horizontal surface and duration of sunshine hours have been determined experimentally for five meteorological stations in Saudi Arabia, namely, Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser sites. Five-years of data covering 1998–2002 period have been used. Suitable Angstrom models have been developed for the global solar radiation estimation as a function of the sunshine duration for each respective sites. Daily averages of monthly solar PV power outputs have been determined using the Angstrom models developed. The effect of the PV cell temperature on the PV efficiency has been considered in calculating the PV power output. The annual average PV output energy has been discussed in all five sites for small loads. The minimum and maximum monthly average values of the daily global solar radiation are found to be 12.09 MJ/m²/d and 30.42 MJ/m²/d for Al-Qaisumah and Al-Jouf in the months of December June, respectively. Minimum monthly average sunshine hours of 5.89 hr were observed in Al-Qaisumah in December while a maximum of 12.92 hr in Al-Jouf in the month of June. Shortest range of sunshine hours of 7.33–10.12 hr was recorded at Abha station. Minimum monthly average Solar PV power of 1.59 MJ/m²/day was obtained at Al-Qaisumah in the month of December and a maximum of 3.39 MJ/m²/day at Al-Jouf in June. The annual PV energy output was found to be 276.04 kWh/m², 257.36 kWh/m², 256.75 kWh/m², 245.44 kWh/m², and 270.95 kWh/m² at Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser stations, respectively. It is found that the Abha site yields the highest solar PV energy among the five sites considered.     

A Simple Process‐Based Snowmelt Routine to Model Spatially Distributed Snow Depth and Snowmelt in the SWAT Model1

Abstract: We present a method to integrate a process‐based (PB) snowmelt model that requires only daily temperature and elevation information into the Soil and Water Assessment Tool (SWAT) model. The model predicts the spatiotemporal snowpack distribution without adding additional complexity, and in fact reduces the number of calibrated parameters. To demonstrate the utility of the PB model, we calibrate the PB and temperature‐index (TI) SWAT models to optimize agreement with stream discharge on a 46‐km² watershed in northwestern Idaho, United States, for 10 individual years and use the calibrated parameters for the year with the best agreement to run the model for 15 remaining years. Stream discharge predictions by the PB and TI model were similar, although the PB model simulated snowmelt more accurately than the TI model for the remaining 15‐year period. Spatial snow distributions predicted by the PB model better matched observations from LandSat imagery and a SNOTEL station. Results for this watershed show that including PB snowmelt in watershed models is feasible, and calibration of TI‐based watershed models against discharge can incorrectly predict snow cover.     

GOES IR AND DUMMY VARIABLE TECHNIQUES FOR LOW TEMPERATURE ASSESSMENT IN FLORIDA1

ABSTRACT: Geostationary Operational Environmental Satellite (GOES) thermal infrared (IR) data were used to study regional cold nocturnal temperature fluctuations that are important in assessing citrus freeze damage. Dummy variables techniques were used to analyze the temperature difference between the east and west sides of Central Florida, which was obtained from both ground measurement minimum temperature (T_GMM) and the GOES mean temperature (T_GOES) on low-temperature nights. The low temperature pattern, which was closely related to the citrus freeze damage pattern in Central Florida, was identified by GOES thermal IR data but not by conventional ground measurements. The low surface temperature in the west side and east side appears to be attributable to differences in soil types, water tables, and drainage classes in both regions.     

Assessment of Future Climate Change Impact on Water Quality of Chungju Lake,South Korea,Using WASP Coupled with SWAT

This study is to evaluate the future potential impact of climate change on the water quality of Chungju Lake using the Water Quality Analysis Simulation Program (WASP). The lake has a storage capacity of 2.75 Gm³, maximum water surface of 65.7 km², and forest‐dominant watershed of 6,642 km². The impact on the lake from the watershed was evaluated by the Soil and Water Assessment Tool (SWAT). The WASP and SWAT were calibrated and validated using the monthly water temperatures from 1998 to 2003, lake water quality data (dissolved oxygen, total nitrogen [T‐N], total phosphorus [T‐P], and chlorophyll‐a [chl‐a]) and daily dam inflow, and monthly stream water quality (sediment, T‐N, and T‐P) data. For the future climate change scenario, the MIROC3.2 HiRes A1B was downscaled for 2020s, 2050s, and 2080s using the Change Factor statistical method. The 2080s temperature and precipitation showed an increase of +4.8°C and +34.4%, respectively, based on a 2000 baseline. For the 2080s watershed T‐N and T‐P loads of up to +87.3 and +19.6%, the 2080s lake T‐N and T‐P concentrations were projected to be 4.00 and 0.030 mg/l from 2.60 and 0.016 mg/l in 2000, respectively. The 2080s chl‐a concentration in the epilimnion and the maximum were 13.97 and 52.45 μg/l compared to 8.64 and 33.48 μg/l in 2000, respectively. The results show that the Chungju Lake will change from its mesotrophic state of 2000 to a eutrophic state by T‐P in the 2020s and by chl‐a in the 2080s. Editor's note: This paper is part of a featured series on Korean Hydrology. The series addresses the need for a new paradigm of river and watershed management for Korea due to climate and land use changes.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

Nitrogen and phosphorus status of soils and trophic state of lakes associated with forage-based beef cattle operations in Florida

Forage-based livestock systems have been implicated as major contributors to deteriorating water quality, particularly for phosphorus (P) from commercial fertilizers and manures affecting surface and ground water quality. Little information exists regarding possible magnitudes of nutrient losses from pastures that are managed for both grazing and hay production and how these might impact adjacent bodies of water. We examined the changes that have occurred in soil fertility levels of rhizoma peanut (Arachis glabrata Benth.)-based beef cattle pastures (n = 4) in Florida from 1988 to 2002. These pastures were managed for grazing in spring followed by haying in late summer and were fertilized annually with P (39 kg P2O5 ha(-1)) and K (68 kg K2O ha(-1)). Additionally, we investigated trends in water quality parameters and trophic state index (TSI) of lakes (n = 3) associated with beef cattle operations from 1993 to 2002. Overall, there was no spatial or temporal buildup of soil P and other crop nutrients despite the annual application of fertilizers and daily in-field loading of animal waste. In fact, soil fertility levels showed a declining trend for crop nutrient levels, especially soil P (y = 146.57 - 8.14 x year; r2= 0.75), even though the fields had a history of P fertilization and the cattle were rotated into the legume fields. Our results indicate that when nutrients are not applied in excess, cow-calf systems are slight exporters of P, K, Ca, and Mg through removal of cut hay. Water quality in lakes associated with cattle production was "good" (30-46 TSI) based on the Florida Water Quality Standard. These findings indicate that properly managed livestock operations may not be major contributors to excess loads of nutrients (especially P) in surface water.     

Climate Change Impacts on Irrigation Water Needs in the jaguaribe River Basin1

Gondim, Rubens S., Marco A.H. de Castro, Aline de H.N. Maia, Sílvio R.M. Evangelista, and Sérgio C. de F. Fuck, Jr., 2012. Climate Change Impacts on Irrigation Water Needs in the Jaguaribe River Basin. Journal of the American Water Resources Association (JAWRA) 48(2): 355‐365. DOI: 10.1111/j.1752‐1688.2011.00620.x Abstract: Climate change is conceptually referred to as a modification to the average of climate variables and their natural variability, due to both natural and anthropogenic driving forces, such as greenhouse gas emissions. Climate change potentially impacts rainfall, temperature, and air humidity, which have relationship with plant evapotranspiration and consequently to irrigation water needs (IWN). The purpose of this research is to assess climate change impacts on irrigation water demand, based on climatic impacts stemming from future greenhouse gas emission scenarios. The study area includes eight municipalities in the Jaguaribe River Basin, located in the Ceará State of semiarid northeast Brazil. The FAO Penman‐Monteith method is used for the calculation of a reference evapotranspiration with limited climatic data. IWN projections are calculated using bias‐corrected climate projections for monthly rainfall and surface temperature derived from the United Kingdom’s Hadley Centre Regional Climate Model simulations. The increase in the average IWN is projected to be 7.9 and 9.1% over the period 2025‐2055 for the A2 and B2 scenarios, respectively with respect to 1961‐1990 baseline.     

Clogging of an Effluent Dominated Semiarid River: A Conceptual Model of Stream‐Aquifer Interactions1

Abstract: Water managers in arid and semiarid regions increasingly view treated wastewater (effluent) as an important water resource. Artificial recharge basins allow effluent to seep into the ground relieving stressed aquifers, however these basins frequently clog due to physical, chemical, and biological processes. Likewise effluent is increasingly used to maintain perennial base flow for dry streambeds, however, little is known about the impact of effluent on streambed hydraulic conductivity and stream‐aquifer interactions. We address this issue by investigating: if a clogging layer forms, how the formation of a clogging layer alters stream‐aquifer connections, and what hydrologic factors control the formation and removal of clogging layers. We focused on the Upper Santa Cruz River, Arizona where effluent from the Nogales International Waste Water Treatment Plant sustains perennial flow. Monthly sampling, along a 30 km river reach, was done with two foci: physical streambed transformations and water source identification using chemical composition. Historical dataset were included to provide a larger context for the work. Results show that localized clogging occurs in the Upper Santa Cruz River. The clogging layers perch the stream and shallow streambed causing desaturation below the streambed. With these results, a conceptual model of clogging is established in the context of a semiarid hydrologic cycle: formation during the hot premonsoon months when flow is nearly constant and removal by large flood flows (>10 m³/s) during the monsoon season. However, if the intensity of flooding during the semiarid hydrologic cycle is lessened, the dependent riparian area can experience a die off. This conceptual model leads us to the conclusion that effluent dominated riparian systems are inherently unstable due to the clogging process. Further understanding of this process could lead to improved ecosystem restoration and management.     

Evaluation of Rio Grande Management Alternatives Using a Surface‐Water/Ground‐Water Model1

Abstract: Previous investigations observed significant seepage losses from the Rio Grande to the shallow aquifer between Socorro and San Antonio, New Mexico. High‐resolution telescopic modeling was used along a 10‐km reach of the Rio Grande and associated drains and canals to evaluate several management alternatives aimed at improving river conveyance efficiency. Observed data consisted of ground‐water and surface‐water elevations, seepage rates along the Rio Grande and associated canals and drains, and borehole geology. Model calibration was achieved by adjusting hydraulic conductivity and specific storage until the output matched observed data. Sensitivity analyses indicated that the system was responsive to changes in hydrogeologic properties, especially when such alterations increased vertical connectivity between layers. The calibrated model predicted that removal of the low flow conveyance channel, a major channel draining the valley, would not only decrease river seepage by 67%, but also decrease total flow through the reach by 75%. The decreased flow through the reach would result in increased water logging and an average increase in ground‐water elevations of 1.21 meter. Simulations of the system with reduced riparian evapotranspiration rates or a relocated river channel also predicted decreased river seepage, but to a much lesser degree.